Create app.py

app.py

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+import streamlit as st
+
+# Load model directly
+from transformers import (AutoTokenizer,
+                          AutoModelForSequenceClassification,
+                          TextClassificationPipeline)
+
+tokenizer = AutoTokenizer.from_pretrained("Hello-SimpleAI/chatgpt-detector-roberta")
+model = AutoModelForSequenceClassification.from_pretrained("Hello-SimpleAI/chatgpt-detector-roberta")
+
+pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer, return_all_scores=True)
+
+def score_and_visualize(text):
+    prediction = pipe([text])
+    f_score = 0
+    f_label = ""
+    label_0 = prediction[0][0]['label']
+    score_0 = prediction[0][0]['score']
+    label_1 = prediction[0][1]['label']
+    score_1 = prediction[0][1]['score']
+    if score_0 > score_1:
+        f_score = (round(score_0))*100
+        f_label = label_0
+    else:
+        f_score = (round(score_1))*100
+        f_label = label_1
+    return f_score, f_label
+
+def main():
+    st.title("Human vs ChatGPT Classification Model")
+
+    # Create an input text box
+    input_text = st.text_area("Enter your text", "")
+
+    # Create a button to trigger model inference
+    if st.button("Analyze"):
+        # Perform inference using the loaded model
+        score, label = score_and_visualize(input_text)
+        st.write("The input text is ", str(score), " ", label , " based.")
+
+if __name__ == "__main__":
+    main()